Recent studies with single-particle tracking in live cells have revealed that chromatin dynamics are directly affected by transcription. However, how transcription alters the chromatin movements followed by changes in the physical properties of chromatin has not been elucidated. Here, we measured diffusion characteristics of chromatin by targeting telomeric DNA repeats with CRISPR-labeling. We found that transcription inhibitors that directly block transcription factors globally increased the movements of chromatin, while the other inhibitor that blocks transcription by DNA intercalating showed an opposite effect. We hypothesized that the increased mobility of chromatin by transcription inhibition and the decreased chromatin movement by a DNA intercalating inhibitor is due to alterations in chromatin rigidity. We also tested how volume confinement of nuclear space affects chromatin movements. We observed decreased chromatin movements under osmotic pressure and with overexpressed chromatin architectural proteins that compact chromatin.
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http://dx.doi.org/10.3389/fcell.2022.822026 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Institute of Science and Technology Austria, AT-3400 Klosterneuburg, Austria.
Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs).
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Center for Nutritional Sciences, Food Science and Human Nutrition Department, College of Agricultural and Life Sciences, University of Florida, Gainesville, FL 32611.
Documented worldwide, impaired immunity is a cardinal signature resulting from loss of dietary zinc, an essential micronutrient. A steady supply of zinc to meet cellular requirements is regulated by an array of zinc transporters. Deletion of the transporter Zip14 (Slc39a14) in mice produced intestinal inflammation.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Malignant gliomas are heterogeneous tumors, mostly incurable, arising in the central nervous system (CNS) driven by genetic, epigenetic, and metabolic aberrations. Mutations in isocitrate dehydrogenase (IDH1/2) enzymes are predominantly found in low-grade gliomas and secondary high-grade gliomas, with IDH1 mutations being more prevalent. Mutant-IDH1/2 confers a gain-of-function activity that favors the conversion of a-ketoglutarate (α-KG) to the oncometabolite 2-hydroxyglutarate (2-HG), resulting in an aberrant hypermethylation phenotype.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016.
Posttranslational modifications (PTMs) of proteins play critical roles in regulating many cellular events. Antibodies targeting site-specific PTMs are essential tools for detecting and enriching PTMs at sites of interest. However, fundamental difficulties in molecular recognition of both PTM and surrounding peptide sequence have hindered the efficient generation of highly sequence-specific anti-PTM antibodies.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
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